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https://issues.apache.org/jira/browse/SPARK-2923?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng resolved SPARK-2923.
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Resolution: Fixed
Fix Version/s: 1.1.0
Issue resolved by pull request 1849
[https://github.com/apache/spark/pull/1849]
> Implement some basic linalg operations in MLlib
> -----------------------------------------------
>
> Key: SPARK-2923
> URL: https://issues.apache.org/jira/browse/SPARK-2923
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Fix For: 1.1.0
>
>
> We use breeze for linear algebra operations. Breeze operations are
> user-friendly but there are some concerns:
> 1. creating temp objects, e.g., `val z = a * x + b * y`
> 2. multi-method is not used in some operators, e.g., `axpy`. If we pass in
> SparseVector as a generic Vector, it will use activeIterator, which is slow
> 3. calling native BLAS if it is available, which might not be good for
> level-1 methods
> Having some basic BLAS operations implemented in MLlib can help simplify the
> current implementation and improve some performance.
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